A Novel Energy Control Digital Twin System with a Resource-Aware Optimal Forecasting Model Selection Scheme

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Abstract
As global energy demand intensifies across industrial, commercial, and residential domains, efficient and accurate energy management and control become crucial. Energy Digital Twins (EDTs), leveraging sensor measurement data and precise time-series forecasting models, offer promising monitoring, prediction, and optimization solutions for such services. Edge computing enables EDTs to deliver real-time management services placed closer to users. However, the existing energy management methodologies may fail to consider the limited resources of edge environments, which may cause service delays and reduced accuracy in management services. To solve this problem, we propose a novel energy control digital twin system with a resource-aware optimal forecasting mode selection scheme. The system dynamically selects optimal forecasting models by integrating statistical features of the input time series with available resources. It employs a two-stage approach: first, it identifies promising models through similarity detection in past time series; second, this initial recommendation is refined by considering the available computing resources to pinpoint the optimal forecasting model. This mechanism enhances adaptability and responsiveness in resource-constrained environments. Utilizing real-world LPG consumption data from 887 sensors, the proposed system achieves forecasting accuracy comparable to previous methods while reducing latency by up to 19 times in low-resource settings.
Funding Information
  • Technology Innovation Program (Development of SDF-Based AI Autonomous Manufacturing Core Technology to Advance the Automobile Industry) funded by the Ministry of Trade, Industry, and Energy (MOTIE), South Korea (RS-2025-00507388)
  • Star Professor Research Program of Korea University of Technology and Education (RS-2025-00507388)

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